S-Transform Time-Frequency Feature Extraction Of Laser Doppler Flowmetry Signal Using Svd Decomposition

Extracting the most important features of input data is extremely important. In this paper we introduced a new approach of feature extraction based on singular value decomposition of the S-transform time-frequency matrix of the laser Doppler flowmetry signal. We used this new approach to study reactive hyperemia blood flow recordings. This kind of bio-data provides an interesting biomedical investigation for estimating microcirculation perfusion. The technique uses the singular vector associated with the time-frequency distribution to characterize the physiological activities behaviors during the hyperemia phenomena